A central concept of a database is to collect organized information and knowledge. Various types of databases and database management systems have been developed and are being developed. Typically, a database has a structural description of information and knowledge held in that database, known as a schema. The schema describes the information and knowledge represented in the database and their relationships. Most database management systems are built around one particular way a database is organized.
The choice of database organization associated with stored data tends to vary according to the type of information being represented. As an example, effective and efficient management of computerized data for scientific applications, including medical data of all kinds, has proven more difficult, in general, than effective management of data in more common, transaction-oriented business applications such as banking, accommodation reservations, on-line purchasing, and the like. Unlike commercial databases, scientific databases often hold a wider spectrum of kinds or types of data. For example, a single dataset may be retained in several forms: raw data as collected; calibrated data corrected for experimental conditions; validated data corrected for known errors; derived products such as graphs and computed values; complex 2D, 3D, 4D, and color-coded visualizations; and interpreted data as represented with respect to various models. Further, information retrieval from scientific data tends to be ad hoc and exploratory in nature, oriented toward a particular question at hand that was not necessarily anticipated at a time that such data were collected. This type of usage is not consistent with the high volume processing of predefined, stored transactions frequently found in business applications. For scientific data to be processed in a meaningful way, it is often essential to record and preserve associated metadata (data about the data) such as operating characteristics and calibration information for particular measuring instruments, ambient conditions when measurements were made, etc. Popular database organizations, including relational, do not work well with requirements such as these.
For one particular example, information in patients' medical records shares the above characteristics of scientific data. In particular, medical information features numerous and often-changing interdependencies among variables and a need for sophisticated processing of temporal information. The situation is aggravated by the ongoing explosive growth in medical knowledge, which implies the continual incorporation of new data elements, new structural components, and new relationships among data elements within a database.
Maintaining databases in view of these deficiencies can come at a substantial cost. In many existing database management systems, adding a new data element or a new structural component to an existing database requires services of an expert database administrator and computer programmer and often necessitates restructuring of a relational database to meet changing needs of analysis.
Many of these deficiencies can be eliminated by incorporation of a knowledgebase with the database, which facilitates storage and retrieval of knowledge, for the purpose of automated reasoning about the data. The knowledgebase consists of data and rules that describe relationships among knowledge elements that are logically consistent. These data and rules are expressed in a particular knowledge representation.
A related ongoing effort relates to the creation of a semantic web. The semantic web is a project that seeks to create a universal medium for information exchange by putting documents with computer-processable meaning (semantics) on the World Wide Web. This approach seeks to leverage descriptive technologies, the Resource Description Framework (RDF) and Web Ontology Language (OWL), and the data-centric, customizable Extensible Markup Language (XML) to provide descriptions that can supplement or replace the content of Web documents. A goal of the semantic web is to enhance the machine-readability of web content by adding meaning (semantics) to the content, so as to facilitate automated information gathering, reasoning and research by computers.